{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:25:15.607595Z",
     "start_time": "2017-11-24T15:25:11.794569Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/Users/attia/Desktop/Work/workenv/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Model,Sequential\n",
    "from keras.layers import Input,Dense, Dropout, Activation, Flatten, Reshape, Conv2D, MaxPooling2D, AveragePooling2D\n",
    "from keras import regularizers\n",
    "from keras.losses import mean_squared_error\n",
    "from keras import losses\n",
    "import matplotlib.patches as patches\n",
    "import numpy as np\n",
    "import dicom\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second step of the algorithm is to train a stacked Auto-encoder in order to get the binary mask of the left ventricule inferred shape within the ROI (region of interest output of CNN). It is performed in two steps : pre-training then fine-tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open results from CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:28:27.868975Z",
     "start_time": "2017-11-24T15:25:17.239012Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape : (495, 64, 64, 1) (495, 1, 32, 32)\n"
     ]
    },
    {
     "data": {
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L4Na57Ogpqbc2UMxcxaxKyMm0rD/LtXe+xs9v11IWO8qH6dmW/Wv1jE/MwcS5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dd220b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size for each layer :\n",
      "Layer, Input Size, Output Size\n",
      "Conv2D_1 (None, 64, 64, 1) (None, 54, 54, 100)\n",
      "Average_Pooling2D_1 (None, 54, 54, 100) (None, 9, 9, 100)\n",
      "Reshape_1 (None, 9, 9, 100) (None, 1, 8100)\n",
      "Dense_1 (None, 1, 8100) (None, 1, 1024)\n",
      "Reshape_2 (None, 1, 1024) (None, 1, 32, 32)\n",
      "Epoch 1/20\n",
      "495/495 [==============================] - 8s 16ms/step - loss: 0.1838 - acc: 0.2115\n",
      "Epoch 2/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.1038 - acc: 0.1429\n",
      "Epoch 3/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.0783 - acc: 0.0616\n",
      "Epoch 4/20\n",
      "495/495 [==============================] - 5s 11ms/step - loss: 0.0621 - acc: 0.0838\n",
      "Epoch 5/20\n",
      "495/495 [==============================] - 5s 11ms/step - loss: 0.0518 - acc: 0.0856\n",
      "Epoch 6/20\n",
      "495/495 [==============================] - 5s 11ms/step - loss: 0.0460 - acc: 0.0728\n",
      "Epoch 7/20\n",
      "495/495 [==============================] - 7s 14ms/step - loss: 0.0407 - acc: 0.0741\n",
      "Epoch 8/20\n",
      "495/495 [==============================] - 7s 13ms/step - loss: 0.0373 - acc: 0.0691\n",
      "Epoch 9/20\n",
      "495/495 [==============================] - 7s 14ms/step - loss: 0.0356 - acc: 0.0620\n",
      "Epoch 10/20\n",
      "495/495 [==============================] - 8s 15ms/step - loss: 0.0343 - acc: 0.0629\n",
      "Epoch 11/20\n",
      "495/495 [==============================] - 6s 13ms/step - loss: 0.0328 - acc: 0.0592\n",
      "Epoch 12/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.0316 - acc: 0.0535\n",
      "Epoch 13/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.0304 - acc: 0.0595\n",
      "Epoch 14/20\n",
      "495/495 [==============================] - 6s 11ms/step - loss: 0.0315 - acc: 0.0545\n",
      "Epoch 15/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.0320 - acc: 0.0614\n",
      "Epoch 16/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.0316 - acc: 0.0607\n",
      "Epoch 17/20\n",
      "495/495 [==============================] - 6s 13ms/step - loss: 0.0317 - acc: 0.0526\n",
      "Epoch 18/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.0317 - acc: 0.0495\n",
      "Epoch 19/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.0314 - acc: 0.0567\n",
      "Epoch 20/20\n",
      "495/495 [==============================] - 6s 12ms/step - loss: 0.0312 - acc: 0.0520\n",
      "Epoch 1/5\n",
      "495/495 [==============================] - 6s 13ms/step - loss: 0.0313 - acc: 0.0482\n",
      "Epoch 2/5\n",
      "495/495 [==============================] - 7s 14ms/step - loss: 0.0308 - acc: 0.0478\n",
      "Epoch 3/5\n",
      "495/495 [==============================] - 7s 14ms/step - loss: 0.0310 - acc: 0.0607\n",
      "Epoch 4/5\n",
      "495/495 [==============================] - 7s 14ms/step - loss: 0.0305 - acc: 0.0521: 1s - loss: 0.0308 - ac\n",
      "Epoch 5/5\n",
      "495/495 [==============================] - 7s 14ms/step - loss: 0.0300 - acc: 0.0536\n"
     ]
    },
    {
     "data": {
      "image/png": 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xvLKcS06dweJPL+TWPz+NU48Zx6IHV7DwS/dz+Y2N/LZpk68NbjZIZNYNJakM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\nMDOzghwWZmZWkMPCzMwK+v9K3GEKUiCsKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1399b8a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored 'y_pred' (ndarray)\n"
     ]
    },
    {
     "data": {
      "image/png": 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DN/KYF0vb8Kjq1W0n4KsKX1aNidzsTPDPXWF9fY/TlzOWV4TiEwv2J0tqZ3Be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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1399b8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX2rI4EsNGXypIYMvNfR/HUM51SK4JYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1392abcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run 'CNN.ipynb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:28:52.686658Z",
     "start_time": "2017-11-24T15:28:52.660203Z"
    }
   },
   "outputs": [],
   "source": [
    "%store -r y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:28:54.766606Z",
     "start_time": "2017-11-24T15:28:54.748173Z"
    }
   },
   "outputs": [],
   "source": [
    "def open_data_AE(y_pred):\n",
    "    \"\"\"\n",
    "    Open dataset from the output of the CNN and\n",
    "    unroll it as 64*64 = vector of 4096 elements\n",
    "    :param y_pred: CNN output\n",
    "    :return: input AE, output\n",
    "    \"\"\"\n",
    "    input_AE = []\n",
    "    contour_experts = []\n",
    "    for j in range(y_pred.shape[0]):\n",
    "        in_AE = cv2.resize(compute_roi_pred(y_pred, j, roi_shape=32)[0],(64 , 64))\n",
    "        contour = cv2.resize(compute_roi_pred(y_pred, j)[2], (64,64), interpolation = cv2.INTERSECT_NONE)\n",
    "        input_AE.append(in_AE)\n",
    "        contour_experts.append(contour)\n",
    "    return np.array(input_AE).reshape((-1, 64*64)), np.array(contour_experts).reshape((-1, 64*64))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked auto-encoder : Construction of 3 autoencoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:28:56.740955Z",
     "start_time": "2017-11-24T15:28:56.730063Z"
    }
   },
   "outputs": [],
   "source": [
    "def customized_loss(y_true, y_pred, alpha=0.0001, beta=3):\n",
    "    \"\"\"\n",
    "    Create a customized loss for the stacked AE.\n",
    "    Linear combination of MSE and KL divergence.\n",
    "    \"\"\"\n",
    "    #customize your own loss components\n",
    "    loss1 = losses.mean_absolute_error(y_true, y_pred)\n",
    "    loss2 = losses.kullback_leibler_divergence(y_true, y_pred)\n",
    "    #adjust the weight between loss components\n",
    "    return (alpha/2) * loss1 + beta * loss2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:28:57.728545Z",
     "start_time": "2017-11-24T15:28:57.006526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((495, 4096), (495, 4096))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, Y_train = open_data_AE(y_pred)\n",
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 1 : ROI + H1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:28:59.024006Z",
     "start_time": "2017-11-24T15:28:58.973118Z"
    }
   },
   "outputs": [],
   "source": [
    "def model1(X_train, get_history=False, verbose=0, param_reg=3*0.001):\n",
    "    \"\"\"\n",
    "    First part of the stacked AE.\n",
    "    Train the AE on the ROI input images.\n",
    "    :param X_train: ROI input image\n",
    "    :param get_history: boolean to return the loss history\n",
    "    :return: encoded ROI image\n",
    "    \"\"\"\n",
    "    autoencoder_0 = Sequential()\n",
    "    encoder_0 = Dense(input_dim=4096, units=100, kernel_regularizer=regularizers.l2(param_reg))\n",
    "    decoder_0 = Dense(input_dim=100, units=4096, kernel_regularizer=regularizers.l2(param_reg))\n",
    "    autoencoder_0.add(encoder_0)\n",
    "    autoencoder_0.add(decoder_0)\n",
    "    autoencoder_0.compile(loss= customized_loss,optimizer='adam', metrics=['accuracy'])\n",
    "    h = autoencoder_0.fit(X_train, X_train, epochs=100, verbose=verbose)\n",
    "\n",
    "    temp_0 = Sequential()\n",
    "    temp_0.add(encoder_0)\n",
    "    temp_0.compile(loss= customized_loss, optimizer='adam', metrics=['accuracy'])\n",
    "    encoded_X = temp_0.predict(X_train, verbose=0)\n",
    "    if get_history:\n",
    "        return h.history['loss'], encoded_X, encoder_0\n",
    "    else:\n",
    "        return encoded_X, encoder_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:29:28.041725Z",
     "start_time": "2017-11-24T15:29:00.355834Z"
    }
   },
   "outputs": [],
   "source": [
    "encoded_X, encoder_0 = model1(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 2 : H1 + H2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:29:31.696930Z",
     "start_time": "2017-11-24T15:29:31.642595Z"
    }
   },
   "outputs": [],
   "source": [
    "def model2(encoded_X, get_history=False, verbose=0, param_reg=3*0.001):\n",
    "    \"\"\"\n",
    "    Second part of the stacked AE.\n",
    "    :param X_train: encoder ROI image\n",
    "    :param get_history: boolean to return the loss history\n",
    "    :return: encoding layer\n",
    "    \"\"\"\n",
    "    autoencoder_1 = Sequential()\n",
    "    encoder_1 = Dense(input_dim=100, units=100, kernel_regularizer=regularizers.l2(param_reg))\n",
    "    decoder_1 = Dense(input_dim=100, units=100, kernel_regularizer=regularizers.l2(param_reg))\n",
    "    autoencoder_1.add(encoder_1)\n",
    "    autoencoder_1.add(decoder_1)\n",
    "    autoencoder_1.compile(loss= customized_loss, optimizer='adam', metrics=['accuracy'])\n",
    "    h = autoencoder_1.fit(encoded_X, encoded_X, epochs=100, verbose=verbose)\n",
    "\n",
    "    temp_0 = Sequential()\n",
    "    temp_0.add(encoder_0)\n",
    "    temp_0.compile(loss= customized_loss, optimizer='adam', metrics=['accuracy'])\n",
    "    encoded_X = temp_0.predict(X_train, verbose=0)\n",
    "    if get_history:\n",
    "        return h.history['loss'], encoder_1\n",
    "    else:\n",
    "        return encoder_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:29:37.568457Z",
     "start_time": "2017-11-24T15:29:31.922106Z"
    }
   },
   "outputs": [],
   "source": [
    "encoder_1 = model2(encoded_X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 3 : H1 + Annotated contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:29:49.504507Z",
     "start_time": "2017-11-24T15:29:49.477784Z"
    }
   },
   "outputs": [],
   "source": [
    "def model3(X_train, Y_train, encoder_0, encoder_1, init='zero',\n",
    "           get_history=False, verbose=0, param_reg=3*0.001):\n",
    "    \"\"\"\n",
    "    Last part of the stacked AE.\n",
    "    :param X_train: ROI input image\n",
    "    :param init: set the initial kernel weights (None for uniform)\n",
    "    :param get_history: boolean to return the loss history\n",
    "    :return: final model\n",
    "    \"\"\"\n",
    "    model = Sequential()\n",
    "    model.add(encoder_0)\n",
    "    model.add(encoder_1)\n",
    "    model.add(Dense(input_dim=100, units=4096, kernel_initializer=init, kernel_regularizer=regularizers.l2(param_reg)))\n",
    "    model.compile(optimizer = 'adam', loss = \"MSE\", metrics=['accuracy'])\n",
    "    h = model.fit(X_train, Y_train, epochs=20, verbose=verbose)\n",
    "    if get_history:\n",
    "        return h.history['loss'], model\n",
    "    else:\n",
    "        return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:30:20.993806Z",
     "start_time": "2017-11-24T15:30:15.839510Z"
    }
   },
   "outputs": [],
   "source": [
    "h, model = model3(X_train, Y_train, encoder_0, encoder_1, get_history=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:30:21.257281Z",
     "start_time": "2017-11-24T15:30:20.996121Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'epochs')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fG3DreJ+HCtX3L+nn6yGSP/qto+tLH+/1fa9EfenyHw5/7gq2rfj+m84fD7Vh\nZmZFzfZDTGZmto8cEGZmVpQDwszMinJAmJlZUQ4IMzMrygFhVkHp6LK/yLoOs1I4IMzMrCgHhFkR\nkj4k6d503P5/lFQr6TVJVyqZu+MOSS3ptsdKuqdgLoXF6fJDJf0mHSjwPklvTl9+gaQb0vkXrino\n6f01JXODPCTp7zL61c1GOCDMRpH0VuAi4OSIOBYYBD5I0mu7KyKOBO4EvpI+5UfAFyLibSS9fYeX\nXwN8N5KBAt9J0vsWklF7Pw0cQdK79mRJ+5EMIXFk+jr/u7y/pdnEHBBmezsNOB74Qzoz2Gkkf8iH\n2DMQ24+BUyQ1Ac0RcWe6fDVwajrmTntE3AQQEX2xZ4yjeyOiO5KB5x4ADiIZZr4PuErSnwNFx0My\nqyQHhNneBKyOiGPTn8Mi4qtFttvXcWp2FdwfJJnJbYBkZM8bSEZUvW0fX9ts2jggzPZ2B3C+pP1h\nZD7pN5F8X85Pt/nPwN0R0Qu8Iuld6fIPA3dGMkNgt6Tz0teYK2neWG+YzgnSFMmQ5J8BjinHL2Y2\nGXVZF2BWbSLiMUl/RTLzVw3JqJ2XATuAE9N1m0jOU0AyfPf30wB4FvhYuvzDwD9K+uv0NS4Y520X\nAjdLaiBpwXx2mn8ts0nzaK5mJZL0WkQsyLoOs0rxISYzMyvKLQgzMyvKLQgzMyvKAWFmZkU5IMzM\nrCgHhJmZFeWAMDOzov4/Nlc0Poc+2FMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x147d52240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(h)), h)\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epochs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:31:27.460359Z",
     "start_time": "2017-11-24T15:31:27.441842Z"
    }
   },
   "outputs": [],
   "source": [
    "def prediction_plot(X, model, idx=None):\n",
    "    \"\"\"\n",
    "    Compute the Inferred shape binary mask using the trained stacked AE model\n",
    "    :param X: dataset to predict\n",
    "    :param model: trained AE model\n",
    "    :param idx: index of the particular picture to return\n",
    "    :return: inferred shape binary mask, infered shape on the MR image\n",
    "    \"\"\"\n",
    "    if not idx:\n",
    "        idx= np.random.randint(len(X))\n",
    "    contours = model.predict(X)\n",
    "    contour = contours[idx].reshape((64,64))\n",
    "    # thresholding\n",
    "    binary = cv2.threshold(contour, 0, 1, cv2.INTERSECT_NONE)\n",
    "    return binary[1], binary[1]*X[idx].reshape(64,64), idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:36:03.018593Z",
     "start_time": "2017-11-24T15:36:02.481032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GfADY4pw7E9gSbovUA40JkdE0JqSuTZvmc87tAfaE7S4zexBYC7wOuDjsdj1w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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x149b57710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(ncols=3, figsize=(10,5))\n",
    "bin_mask, img, idx = prediction_plot(X_train, model)\n",
    "ax[0].imshow(Y_train[idx].reshape((64,64)))\n",
    "ax[0].set_title('Expert binary mask')\n",
    "ax[1].imshow(bin_mask)\n",
    "ax[1].set_title('Predicted binary mask')\n",
    "ax[2].imshow(img)\n",
    "ax[2].set_title('Predicted LV region')\n",
    "_ = plt.suptitle('Image %s' % idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:41:21.781660Z",
     "start_time": "2017-11-24T15:41:21.773550Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1.,  1.,  1.],\n",
       "       [ 0.,  1.,  1.,  0.,  1.],\n",
       "       [ 0.,  1.,  1.,  0.,  1.],\n",
       "       [ 1.,  0.,  1.,  1.,  1.],\n",
       "       [ 0.,  1.,  1.,  1.,  1.]], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred[15:20, 15:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T15:39:59.347914Z",
     "start_time": "2017-11-24T15:39:59.293161Z"
    }
   },
   "outputs": [],
   "source": [
    "idx = 345\n",
    "Y_true = Y_train[345].reshape((64,64))\n",
    "Y_pred = prediction_plot(X_train, model, idx=345)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$A_{a} : \\text{predicted contour}$  \n",
    "$A_{m} : \\text{manual contour}$\n",
    "\n",
    "**Dice metric**  \n",
    "Measuring the contour overlap   \n",
    "$ QS = \\frac{2 |A_{a} \\bigcap A_{m}|}{|A_{a}|+|A_{m}|} $\n",
    "\n",
    "** Conformity coefficient**  \n",
    "Measuring the  ratio of the number of mis-segmented pixels  \n",
    "$ CC = \\frac{3DM-2}{DM} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T16:02:30.247155Z",
     "start_time": "2017-11-24T16:02:30.242314Z"
    }
   },
   "outputs": [],
   "source": [
    "def dice_metric(X, Y):\n",
    "    return np.sum(X[Y==1])*2.0 / (np.sum(X) + np.sum(Y))\n",
    "\n",
    "def conformity_coefficient(X, Y):\n",
    "    return (3*dice_metric(X,Y)-2)/dice_metric(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-24T16:02:48.261781Z",
     "start_time": "2017-11-24T16:02:48.257463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.70360360360360363, 0.15749039692701661)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dice_metric(Y_pred, Y_true), conformity_coefficient(Y_pred, Y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
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  "toc": {
   "nav_menu": {},
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